Sequence specificity, statistical potentials, and three-dimensional structure prediction with self-correcting distance geometry calculations of β-sheet formation in proteins

Hongyao Zhu, Werner Braun

Research output: Contribution to journalArticlepeer-review

58 Scopus citations

Abstract

A statistical analysis of a representative data set of 169 known protein structures was used to analyze the specificity of residue interactions between spatial neighboring strands in β-sheets. Pairwise potentials were derived from the frequency of residue pairs in nearest contact, second nearest and third nearest contacts across neighboring β-strands compared to the expected frequency of residue pairs in a random model. A pseudo-energy function based on these statistical pairwise potentials recognized native β- sheets among possible alternative pairings. The native pairing was found within the three lowest energies in 73% of the cases in the training data set and in 63% of β-sheets in a test data set of 67 proteins, which were not part of the training set. The energy function was also used to detect tripeptides, which occur frequently in β-sheets of native proteins. The majority of native partners of tripeptides were distributed in a low energy range. Self-correcting distance geometry (SECODG) calculations using distance constraints sets derived from possible low energy pairing of β-strands uniquely identified the native pairing of the β-sheet in pancreatic trypsin inhibitor (BPTI). These results will be useful for predicting the structure of proteins from their amino acid sequence as well as for the design of proteins containing β-sheets.

Original languageEnglish (US)
Pages (from-to)326-342
Number of pages17
JournalProtein Science
Volume8
Issue number2
DOIs
StatePublished - 1999
Externally publishedYes

Keywords

  • Self-correcting distance geometry calculations
  • Statistical potentials
  • Tertiary structure
  • β-strand alignment recognition
  • β-strand pairing

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Biology

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